As observation technologies through satellite evolve, high-precision (or definition), high-resolution satellite images have been able to be acquired. The utilization of these satellite images is increasing in many areas, such as monitoring and analysis of crops, land management, cartography, defense, environmental management, etc. However, very large amount of data have to be transferred and processed for high-precision (or definition), high-resolution satellite images.
Therefore, highly efficient data compression techniques are required for delivering large amounts of satellite image data from satellite to the ground by using limited frequency resources, and so far mostly DCT-based JPEG-like compression method has been adopted for this satellite image data compression. However, DCT-based compression method occurs blocking noise, which causes the distortion factor to be generated and then the quality of the satellite images to be degraded, and thus additional processing for compensating the distortion was required.
In order to complement the disadvantages of DCT-based JPEG-like methods and to get a higher image quality with the same compression ratio, wavelet-based compression technique used in JPEG2000 was applied, and CCSDS, which is an international consultative body with respect to data communications in the field of space for the compression of satellite images, recommends wavelet-based image compression methods optimized for the images observed in satellites.
FIG. 1 shows the figure of LEO (Low Earth Orbit) satellite observations by the prior arts. LEO satellites are currently used for the acquisition of high resolution satellite images, and ground observations are performed by photographing the surface of ground area within observable viewing angle and swath in the middle of moving the satellite along its orbit and transmitting images to the ground.
FIG. 2 shows the shape of step-by-step process of satellite observations images by conventional prior arts, and the shape becomes the form of strip. The number of column of image in the shape of strip is fixed value in accordance with the resolution and swath of the satellite image, the number of line is variable over time, and the value is very large from hundreds of thousands to millions of lines. Satellite image data transferred to the ground is stored as image product after processing the image data at each level, or provided to users. The number of each level and the operations performed in each level can be differently designated depending on satellite, but the level can be divided into level, 0 (1), 1A (2), 1R (3), 1G (4) in general. Each level is defined as follows.                Level 0 (1): Images made in the form of strip by channel decoding, decrypting and decompressing the data received from the satellite.        Level 1A (2): Performing tasks for combining images in each observation wavelength by using level 0 images or some compensations tasks. The shape (form) of image is in the form of strip like the image in level 0.        Level 1R (3): Performing tasks compensating radiometric characteristics by using level 1A images. The shape (form) of image is in the form of scene.        Level 1G (4): Performing tasks compensating geometric characteristics by using level 1R images, and outputting images being mapped into a map.        
In the most of the conventional satellite image processing method, compressed images are transmitted from satellite as shown in FIG. 2 in the form of strip and then decompressed. And images ordered by a user are produced by processing the radiation correction and geometric correction. The uncompressed images are stored in a long-term storage in the state that various calibrations are processed in order to produce additional images. The shape to be stored in a long-term storage is level 0 (1) or level 1A (2). Level 1R (3) and level 1G (4) are the form in which images are provided to a buyer ordering the images.
This kind of processing method has the following disadvantages.
First, images are stored in long-term storage in uncompressed state, and thus the construction costs and TCO (Total Cost Ownership) for constructing storage system for storing data are severely required.
Secondly, images are stored by applying algorithms, radiation calibration, geometric correction, etc., to compensate for various distortions of satellite prior to storing images in long-term storage, and thus it is impossible to improve the quality of images for the previous images prior to the application time point of the improved compensation algorithms because original images cannot be recovered even improved compensation algorithms are developed.
Thirdly, it takes a long waiting time for a user to acquire usable images, because of uncompressing the strip images transmitted from satellite and performing calibration process for the images.
Fourthly, in the case of continuously storing compressed satellite image data in the wavelet-based compression method recommended from CCSDS, if an error occurs in the data due to noise during transmitting the data from satellite, it is impossible to process the remaining data from the part where the error occurs because there is no identifier that can identify the compressed segment in the compressed satellite image data.
FIG. 3 shows the structure of a conventional compressed satellite image file, where it comprises heather (10) and data (11). There is a fixed pattern identifier which can identify the beginning of each compressed segment in the header, and thus the image data in any compressed interval can be independently decompressed. However, there is a problem that the entire frame should be decompressed in a sequence in order to decompress each compressed section because there is no fixed pattern identifier (marker) being able to identify each segment in the header of wavelet-based compression method recommended in CCSDS.
A method of approaching any line of a strip type image is disclosed in Korean Patent No. 0945733, in which the method allows random access, not sequential, to an images section requiring decompression by using index file which is made for the information for each block interval of a compressed image. Wherein, index information consisting of starting position and the length for the interval of image line block data unit is generated. There is a fixed pattern identifier identifying the start point in the image line block data, and thus index information can be generated by identifying this identifier. Moreover, the index for random access is generated with physical location information of each image line data in the stored file for the line data of line-by-line compressed image. This method is available in the existing DCT-based compression method, but wavelet-based compression scheme does not have distinguishable identifier for a compressed segment, and it is the reason why the relationship between each compressed interval is not independent each other due to the characteristics of wavelet transform.